Jurnal Ilmiah Rekayasa Pertanian dan Biosistem

Non-Destructive Moisture Content Prediction Model for Corn Starch Based on Near-Infrared Spectroscopy and Chemometrics

Thu, 26 Mar 2026 00:00:00 +0700

Moisture content is a critical quality attribute of corn starch that affects shelf life, functional performance, and commercial value. This study developed and externally validated a rapid and non-destructive method to quantify corn starch moisture using near-infrared (NIR) spectroscopy and chemometric/machine-learning regression. Commercial corn starch was conditioned at approximately 76% relative humidity (saturated NaCl) for 20 days to generate moisture variability, and spectra were acquired using a SpectraStar XT-R instrument (900-2200 nm). Three spectral pre-processing strategies (MSC, SNV, and Savitzky-Golay first derivative) were evaluated prior to model development. A total of 951 samples were split by stratified sampling into calibration (70%, n = 666) and independent prediction (30%, n = 285) sets. Three models were compared: partial least squares regression (PLSR), support vector regression optimized by particle swarm optimization (SVR-PSO), and a one-dimensional convolutional neural network (1D-CNN). The best performance was achieved by PLSR with SNV (R²_p = 0.929, RMSE_p = 0.274%, RPD = 3.755), while SVR-PSO with MSC showed comparable accuracy (R²_p = 0.929, RMSE_p = 0.273%, RPD = 3.762). The 1D-CNN yielded lower predictive performance (best R²_p = 0.841). Overall, NIR spectroscopy combined with optimized pre-processing and conventional regression models provides an accurate alternative to gravimetric drying for quality control of corn starch.

Biochar Production from Agricultural Waste for Sustainable Soil Management and Climate Change Mitigation

Dick Dick Maulana, Hee-Deung Park — Thu, 26 Mar 2026 00:00:00 +0700

Climate change and land degradation threaten global ecology and food security. Biochar, produced via oxygen-limited thermochemical conversion of agricultural waste, offers a multifunctional solution. This narrative review with meta-analysis of quantitative outcomes (2010-2025 literature) synthesizes biochar production techniques, physicochemical properties, and sustainable agriculture applications, demonstrating biochar's critical role in soil health improvement and climate change mitigation. Studies were selected based on: (1) peer-reviewed English-language journals, (2) agricultural waste feedstocks, (3) quantitative soil/crop/environmental outcomes, (4) field-relevant research, and (5) methodological rigor. Recent research documents biochar's transformative effects on soil physical (water retention +18-25% in sandy soils), chemical (pH 7-11, CEC enhancement), and biological properties, particularly in degraded, acidic, or nutrient-poor soils. Performance depends on feedstock type (agricultural residues, woody biomass, manure), pyrolysis temperature (350-700°C), and residence time (0.5-4 hours). Field trials report yield increases of 10-340% (meta-analysis range), carbon sequestration of 3.7 t CO2eq/t stable biochar, and GHG reductions of 30-50% N2O and 12-25% CH4 across diverse soil-crop systems. Co-application with fertilizers/compost optimizes nutrient use efficiency, though performance varies by soil type and environment, necessitating site-specific strategies. Economic barriers, production costs, and carbon market access influence adoption. Critical gaps include long-term field data and mechanistic insights into biochar-soil-microbe interactions. Future priorities encompass engineered biochar (nanoparticle-modified for targeted functions), precision applications, and policy frameworks. Strategic, evidence-based deployment protocols will maximize benefits while acknowledging context-dependent limitations, quality variability, and trade-offs requiring careful management.

Monitoring Irrigation Management in Melon Cultivation using IoT

Fadli Irsyad, Khairil Agustoria — Thu, 26 Mar 2026 00:00:00 +0700

Uncertainty in irrigation water supply in agricultural land is often one of the main factors causing a decline in crop production capacity. Manual irrigation not only requires additional labor but also consumes considerable time, thereby reducing farmers’ work efficiency. Therefore, innovations are needed in the form of a more modern, efficient irrigation system capable of adjusting to crop requirements in real time. The main objective of this study is to develop a drip irrigation monitoring and control system based on the Internet of Things (IoT) to meet the water and nutrient needs of melon plants. The designed system utilizes a capacitive soil moisture sensor, a soil pH sensor, and a DHT-22 temperature and humidity sensor. All sensors are connected to an ESP32 microcontroller, which processes the data and automatically transmits it to a spreadsheet application for recording and monitoring purposes. The fertigation system has dimensions of 510 × 150 cm and applies drip irrigation technology controlled automatically based on soil moisture and soil acidity (pH) values. The results of the correlation analysis showed that the average coefficients of determination (R²) for the soil moisture sensor, soil pH sensor, and DHT-22 sensor were 0.8395, 0.9896, and 0.984, respectively. Plant observations indicated that the average plant height in the automated system was 48.19 cm, which was higher than the control plants at 43.69 cm. Thus, the IoT-based fertigation system proved to operate effectively, more efficiently, and with better performance compared to conventional methods.

Analysis of Evapotranspiration and Water Balance of Corn (Zea mays L.) using Cropwat 8.0

Sillviana Yuan Arista, Dewi Fortuna, Yulfita Farni — Thu, 26 Mar 2026 00:00:00 +0700

Cropwat 8.0 is a software that functions to estimate the needs of plant water and irrigation. This device performs its calculations by analyzing data on soil type, climate conditions, and the characteristics of certain plants. This study was designed to analyze evapotranspiration and water balance in corn plants (Zea mays L.). This analysis is important to analyzing evapotranspiration, water balance, and determining the optimal planting pattern of corn (Zea mays L.) in Rengas village, Bandung Muaro Jambi Regency, using the Cropwat 8.0 application. This research was carried out using quantitative methods with several research stages to be carried out. The research results showed that the highest ETo value was in March at 104,97 mm/month, while the lowest ETo was in June at 85,69 mm/month. The average annual ETo was 1136,08 mm/month. The results of the water balance analysis show a water surplus throughout the year, namely from January to December, where the average monthly rainfall in Rengas Village, Bandung, Muaro Jambi Regency for the period 2013-2022 is between 130,4-305,8 mm, exceeding the evapotranspiration value between 2,99-3,86 mm/day, so the availability of water in corn plants is sufficient. The corn planting pattern carried out in Rengas Village, Bandung, Muaro Jambi Regency, planting in May and harvesting in early September is suitable based on simulations using the Cropwat 8.0 application to analyze evapotranspiration and water balance of corn plants (Zea mays L.).

Optimization of Potato Seedling Cultivation in Greenhouse with NPK Sensor-Based IoT Technology

Ade Ismail, Vipkas Al Hadid Firdaus, Kadek Suarjuna Batubulan, Luqman Affandi — Thu, 26 Mar 2026 00:00:00 +0700

This study aims to design and implement an Internet of Things (IoT)-based Smart Precision Farming system to optimize potato seedling cultivation through precise and efficient nutrient management. The system is developed using a dual-node ESP32 microcontroller architecture for real-time microclimate data acquisition and soil NPK level monitoring using an industrial-grade sensor with the RS485 Modbus communication protocol. The automated fertigation strategy is implemented using Mamdani Fuzzy Logic integrated with MQTT, Node-RED, and Grafana platforms for data visualization. Technical performance evaluation reveals high system reliability, with a Mean Absolute Percentage Error (MAPE) of 2.62% for the NPK sensor and actuator response latency of <500 ms. Greenhouse implementation proves that transitioning from schedule-based to demand-based fertilization significantly increases fertilizer efficiency by up to 30.3%. This reduction in fertilizer volume does not trigger nutrient deficiency but instead optimally stimulates the agronomic growth of potato seedlings (Sig. < 0.05), indicated by a 15.9% increase in plant height and a 33.3% increase in the number of leaves. The application of this technology offers a concrete and measurable solution to minimize fertilizer waste while improving growth quality in the horticultural seedling phase.

Characterization of Tumeric Simplicia (Curcuma longa L) using Sun Drying and Fluidized Bed Drying

Arla Klisya Safitri, Supriyanto Supriyanto, Mojiono — Thu, 26 Mar 2026 00:00:00 +0700

Fresh turmeric contains high moisture content that accelerates deterioration and reduces quality and shelf life; therefore, an effective drying method is required to preserve the quality of turmeric simplicia. This study aimed to compare the characteristics of turmeric simplicia dried using sun drying and a Fluidized Bed Dryer (FBD). The experiment was arranged in a Completely Randomized Design (CRD) with two drying treatments and three replications. FBD drying was conducted at 50°C with an airflow velocity of 2 m/s until the final moisture content reached <10% (wet basis), while sun drying was performed until the same moisture target was achieved. The observed parameters included weight loss, drying rate, shrinkage area, color, and curcumin content. Data were analyzed using analysis of variance (ANOVA) at a 95% confidence level (α = 0.05), followed by a post hoc test when significant differences were detected. The results showed that FBD drying produced a faster drying rate, better maintained material shape and size with lower shrinkage, resulted in brighter color, and preserved higher curcumin content (11.23 mg/g) compared to sun drying (9.67 mg/g). These findings indicate that the FBD method is more efficient and capable of producing higher-quality turmeric simplicia for herbal and pharmaceutical applications.

Non-Destructive Oil Palm Ripeness Detection using Multisensor Electronic Nose and Random Forest

Tia Purnami, Sri Lestari, Shabri Putra Wirman, Neneng Fitrya — Thu, 26 Mar 2026 00:00:00 +0700

Accurate determination of oil palm fresh fruit bunch (FFB) ripeness is crucial to ensure crude palm oil (CPO) quality, yet conventional visual inspection remains subjective and inconsistent. This study proposes a non-destructive ripeness detection system based on a multisensor electronic nose combined with a Random Forest classifier. The system employs five metal oxide semiconductor gas sensors (MQ-2, MQ-3, MQ-4, MQ-5, and MQ-135) integrated with an ESP32 microcontroller to capture volatile organic compounds emitted during fruit ripening. Sensor signals were transformed into seven statistical features, including maximum, minimum, delta, mean, standard deviation, area under the curve, and slope. The dataset was divided into 70% training data and 30% testing data, and model performance was evaluated using a confusion matrix. The results demonstrated an accuracy of 95.3%, precision of 94.8%, recall of 95.1%, and an F1-score of 95.0%. The proposed system successfully classified oil palm fruits into four ripeness levels: unripe, underripe, ripe, and overripe. These findings indicate that the developed electronic nose system provides an objective and reliable approach for oil palm ripeness assessment, with strong potential to support harvesting decisions and quality control in the palm oil industry.